Figure2C

Load Data

library(gdata)
dataset <- read.delim("raw_data/Figure2C.txt", stringsAsFactors = FALSE)

dataset$genotype <- factor(gsub(" ","",dataset$genotype))
dataset$experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each= length(levels(dataset$genotype))))

dataset$UID <- factor(paste(dataset$experiment, dataset$genotype))

kable(dataset, row.names = F)

genotype	NT	X30.nM	X300.nM	X3000.nM	X30000.nM	experiment	UID
WT	22.01058	14.99513	27.16387	40.27288	38.033250	exp1	exp1 WT
PARP1KO	24.99867	28.56546	32.87948	28.45025	28.545320	exp1	exp1 PARP1KO
ALC1KO	12.11620	39.52356	45.58298	61.03559	64.132780	exp1	exp1 ALC1KO
ALC1KOPARP1KO	12.52204	14.80034	19.89682	26.65366	20.054400	exp1	exp1 ALC1KOPARP1KO
WT	27.32625	31.84061	25.66672	29.00732	49.918700	exp2	exp2 WT
PARP1KO	98.22148	135.57802	114.79064	85.43817	67.054973	exp2	exp2 PARP1KO
ALC1KO	30.10425	120.77006	156.36509	152.38575	122.686347	exp2	exp2 ALC1KO
ALC1KOPARP1KO	38.10620	20.10778	23.18652	24.74054	6.578637	exp2	exp2 ALC1KOPARP1KO
WT	42.88251	45.64701	47.50491	52.09927	84.335243	exp3	exp3 WT
PARP1KO	51.33273	91.06651	36.30963	30.68799	83.422833	exp3	exp3 PARP1KO
ALC1KO	54.28341	141.70070	192.99382	189.15404	156.706603	exp3	exp3 ALC1KO
ALC1KOPARP1KO	27.83733	50.46166	32.71616	28.70225	33.340863	exp3	exp3 ALC1KOPARP1KO

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "treatment", value.name = "Outcome")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT")


dataset$olaparib <- gsub("NT","1",dataset$treatment)
dataset$olaparib <- gsub("X|nM","",dataset$olaparib)
dataset$olaparib <- log10(as.integer(dataset$olaparib))



dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Outcome[dataset$UID == uid])
}

dataset$NormOutcome <- dataset$Outcome / dataset$Offset



dataset$Offset2 <- NA
for(gid in levels(dataset$genotype)){
        dataset$Offset2[dataset$genotype == gid] <- mean(dataset$NormOutcome[dataset$genotype == gid & dataset$olaparib == 0])
}

dataset$NormOutcome2 <- dataset$NormOutcome / dataset$Offset2




kable(dataset, row.names = F)

genotype	experiment	UID	treatment	Outcome	olaparib	Offset	NormOutcome	Offset2	NormOutcome2
WT	exp1	exp1 WT	NT	22.010580	0.000000	28.49514	0.7724327	0.7978993	0.9680830
PARP1KO	exp1	exp1 PARP1KO	NT	24.998670	0.000000	28.68784	0.8714031	0.9093397	0.9582812
ALC1KO	exp1	exp1 ALC1KO	NT	12.116200	0.000000	44.47822	0.2724075	0.3000842	0.9077700
ALC1KOPARP1KO	exp1	exp1 ALC1KOPARP1KO	NT	12.522040	0.000000	18.78545	0.6665818	1.0537221	0.6325973
WT	exp2	exp2 WT	NT	27.326250	0.000000	32.75192	0.8343404	0.7978993	1.0456713
PARP1KO	exp2	exp2 PARP1KO	NT	98.221477	0.000000	100.21666	0.9800913	0.9093397	1.0778056
ALC1KO	exp2	exp2 ALC1KO	NT	30.104250	0.000000	116.46230	0.2584892	0.3000842	0.8613889
ALC1KOPARP1KO	exp2	exp2 ALC1KOPARP1KO	NT	38.106200	0.000000	22.54393	1.6903084	1.0537221	1.6041311
WT	exp3	exp3 WT	NT	42.882513	0.000000	54.49379	0.7869248	0.7978993	0.9862457
PARP1KO	exp3	exp3 PARP1KO	NT	51.332727	0.000000	58.56394	0.8765245	0.9093397	0.9639132
ALC1KO	exp3	exp3 ALC1KO	NT	54.283413	0.000000	146.96772	0.3693560	0.3000842	1.2308412
ALC1KOPARP1KO	exp3	exp3 ALC1KOPARP1KO	NT	27.837327	0.000000	34.61165	0.8042761	1.0537221	0.7632716
WT	exp1	exp1 WT	X30.nM	14.995130	1.477121	28.49514	0.5262346	0.7978993	0.6595251
PARP1KO	exp1	exp1 PARP1KO	X30.nM	28.565460	1.477121	28.68784	0.9957342	0.9093397	1.0950080
ALC1KO	exp1	exp1 ALC1KO	X30.nM	39.523560	1.477121	44.47822	0.8886048	0.3000842	2.9611843
ALC1KOPARP1KO	exp1	exp1 ALC1KOPARP1KO	X30.nM	14.800340	1.477121	18.78545	0.7878618	1.0537221	0.7476941
WT	exp2	exp2 WT	X30.nM	31.840607	1.477121	32.75192	0.9721753	0.7978993	1.2184185
PARP1KO	exp2	exp2 PARP1KO	X30.nM	135.578023	1.477121	100.21666	1.3528492	0.9093397	1.4877270
ALC1KO	exp2	exp2 ALC1KO	X30.nM	120.770057	1.477121	116.46230	1.0369884	0.3000842	3.4556577
ALC1KOPARP1KO	exp2	exp2 ALC1KOPARP1KO	X30.nM	20.107777	1.477121	22.54393	0.8919373	1.0537221	0.8464636
WT	exp3	exp3 WT	X30.nM	45.647010	1.477121	54.49379	0.8376553	0.7978993	1.0498258
PARP1KO	exp3	exp3 PARP1KO	X30.nM	91.066513	1.477121	58.56394	1.5549930	0.9093397	1.7100244
ALC1KO	exp3	exp3 ALC1KO	X30.nM	141.700703	1.477121	146.96772	0.9641621	0.3000842	3.2129714
ALC1KOPARP1KO	exp3	exp3 ALC1KOPARP1KO	X30.nM	50.461660	1.477121	34.61165	1.4579385	1.0537221	1.3836081
WT	exp1	exp1 WT	X300.nM	27.163870	2.477121	28.49514	0.9532807	0.7978993	1.1947382
PARP1KO	exp1	exp1 PARP1KO	X300.nM	32.879480	2.477121	28.68784	1.1461122	0.9093397	1.2603786
ALC1KO	exp1	exp1 ALC1KO	X300.nM	45.582980	2.477121	44.47822	1.0248382	0.3000842	3.4151682
ALC1KOPARP1KO	exp1	exp1 ALC1KOPARP1KO	X300.nM	19.896820	2.477121	18.78545	1.0591611	1.0537221	1.0051617
WT	exp2	exp2 WT	X300.nM	25.666717	2.477121	32.75192	0.7836706	0.7978993	0.9821673
PARP1KO	exp2	exp2 PARP1KO	X300.nM	114.790640	2.477121	100.21666	1.1454248	0.9093397	1.2596226
ALC1KO	exp2	exp2 ALC1KO	X300.nM	156.365093	2.477121	116.46230	1.3426241	0.3000842	4.4741573
ALC1KOPARP1KO	exp2	exp2 ALC1KOPARP1KO	X300.nM	23.186517	2.477121	22.54393	1.0285036	1.0537221	0.9760672
WT	exp3	exp3 WT	X300.nM	47.504913	2.477121	54.49379	0.8717491	0.7978993	1.0925553
PARP1KO	exp3	exp3 PARP1KO	X300.nM	36.309627	2.477121	58.56394	0.6199998	0.9093397	0.6818132
ALC1KO	exp3	exp3 ALC1KO	X300.nM	192.993817	2.477121	146.96772	1.3131715	0.3000842	4.3760095
ALC1KOPARP1KO	exp3	exp3 ALC1KOPARP1KO	X300.nM	32.716163	2.477121	34.61165	0.9452355	1.0537221	0.8970444
WT	exp1	exp1 WT	X3000.nM	40.272880	3.477121	28.49514	1.4133244	0.7978993	1.7713067
PARP1KO	exp1	exp1 PARP1KO	X3000.nM	28.450250	3.477121	28.68784	0.9917182	0.9093397	1.0905916
ALC1KO	exp1	exp1 ALC1KO	X3000.nM	61.035590	3.477121	44.47822	1.3722579	0.3000842	4.5729087
ALC1KOPARP1KO	exp1	exp1 ALC1KOPARP1KO	X3000.nM	26.653660	3.477121	18.78545	1.4188458	1.0537221	1.3465086
WT	exp2	exp2 WT	X3000.nM	29.007323	3.477121	32.75192	0.8856679	0.7978993	1.1099996
PARP1KO	exp2	exp2 PARP1KO	X3000.nM	85.438167	3.477121	100.21666	0.8525346	0.9093397	0.9375315
ALC1KO	exp2	exp2 ALC1KO	X3000.nM	152.385750	3.477121	116.46230	1.3084556	0.3000842	4.3602943
ALC1KOPARP1KO	exp2	exp2 ALC1KOPARP1KO	X3000.nM	24.740540	3.477121	22.54393	1.0974367	1.0537221	1.0414859
WT	exp3	exp3 WT	X3000.nM	52.099270	3.477121	54.49379	0.9560588	0.7978993	1.1982199
PARP1KO	exp3	exp3 PARP1KO	X3000.nM	30.687987	3.477121	58.56394	0.5240083	0.9093397	0.5762514
ALC1KO	exp3	exp3 ALC1KO	X3000.nM	189.154040	3.477121	146.96772	1.2870448	0.3000842	4.2889450
ALC1KOPARP1KO	exp3	exp3 ALC1KOPARP1KO	X3000.nM	28.702250	3.477121	34.61165	0.8292655	1.0537221	0.7869869
WT	exp1	exp1 WT	X30000.nM	38.033250	4.477121	28.49514	1.3347275	0.7978993	1.6728020
PARP1KO	exp1	exp1 PARP1KO	X30000.nM	28.545320	4.477121	28.68784	0.9950322	0.9093397	1.0942360
ALC1KO	exp1	exp1 ALC1KO	X30000.nM	64.132780	4.477121	44.47822	1.4418917	0.3000842	4.8049564
ALC1KOPARP1KO	exp1	exp1 ALC1KOPARP1KO	X30000.nM	20.054400	4.477121	18.78545	1.0675495	1.0537221	1.0131224
WT	exp2	exp2 WT	X30000.nM	49.918700	4.477121	32.75192	1.5241458	0.7978993	1.9101982
PARP1KO	exp2	exp2 PARP1KO	X30000.nM	67.054973	4.477121	100.21666	0.6691001	0.9093397	0.7358088
ALC1KO	exp2	exp2 ALC1KO	X30000.nM	122.686347	4.477121	116.46230	1.0534426	0.3000842	3.5104895
ALC1KOPARP1KO	exp2	exp2 ALC1KOPARP1KO	X30000.nM	6.578637	4.477121	22.54393	0.2918140	1.0537221	0.2769364
WT	exp3	exp3 WT	X30000.nM	84.335243	4.477121	54.49379	1.5476120	0.7978993	1.9396082
PARP1KO	exp3	exp3 PARP1KO	X30000.nM	83.422833	4.477121	58.56394	1.4244745	0.9093397	1.5664933
ALC1KO	exp3	exp3 ALC1KO	X30000.nM	156.706603	4.477121	146.96772	1.0662655	0.3000842	3.5532205
ALC1KOPARP1KO	exp3	exp3 ALC1KOPARP1KO	X30000.nM	33.340863	4.477121	34.61165	0.9632843	1.0537221	0.9141731

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=olaparib, y=Outcome)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=genotype)) +
        geom_point(aes(colour=genotype, shape=experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#000080","#808080","#800000"))

# norm
ggplot(dataset, aes(x=olaparib, y=NormOutcome)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=genotype)) +
        geom_point(aes(colour=genotype, shape=experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#000080","#808080","#800000"))

# norm2
ggplot(dataset, aes(x=olaparib, y=NormOutcome2)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=genotype)) +
        geom_point(aes(colour=genotype, shape=experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Olaparib (log10 nM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#000080","#808080","#800000"))

dataplot <- aggregate(NormOutcome2 ~ genotype+treatment, data = dataset, mean)
dataplot$se <- aggregate(NormOutcome2 ~ genotype+treatment, data = dataset, FUN = function(x){ sd(x)/sqrt(length(x))})$NormOutcome2
dataplot$treatment <- factor(gsub("X|.nM","", dataplot$treatment)) 

dataplot$genotype <- factor(dataplot$genotype, levels = c("WT","PARP1KO","ALC1KO","ALC1KOPARP1KO"))
dataplot$treatment <- relevel(dataplot$treatment, ref = "NT")


ggplot(dataplot, aes(x=treatment, y=NormOutcome2, fill = genotype)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_bar(stat="identity", position=position_dodge()) +
        geom_errorbar(aes(ymin=NormOutcome2-se, ymax=NormOutcome2+se),width=.2, position=position_dodge(.9)) +
        ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
        xlab(label = "Olaparib (nM)") +
        ylim(0, 6) +
        scale_fill_manual(values=c("#000000","#800000","#000080","#808080"))

library(Cairo)

cairo_pdf("Figure2C_v1.pdf", width = 8, height = 4, family = "Arial")

ggplot(dataplot, aes(x=treatment, y=NormOutcome2, fill = genotype)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_bar(stat="identity", position=position_dodge()) +
        geom_errorbar(aes(ymin=NormOutcome2-se, ymax=NormOutcome2+se),width=.2, position=position_dodge(.9)) +
        ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
        xlab(label = "Olaparib (nM)") +
        ylim(0, 6) +
        scale_fill_manual(values=c("#000000","#800000","#000080","#808080"))

dev.off()

## quartz_off_screen 
##                 2

dataset$genotype <- factor(dataset$genotype, levels = c("WT","PARP1KO","ALC1KO","ALC1KOPARP1KO"))
dataset$treatment <- relevel(dataset$treatment, ref = "NT")
dataset$treatment <- factor(gsub("X|.nM","", dataset$treatment), levels =c("NT","30","300","3000","30000") ) 


cairo_pdf("Figure2C_v2.pdf", width = 8, height = 4, family = "Arial")

ggplot(dataplot, aes(x=treatment, y=NormOutcome2, fill = genotype)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_bar(stat="identity", position=position_dodge()) +
        geom_bar(stat="identity", position=position_dodge(), colour="black") +
        ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
        xlab(label = "Olaparib (nM)") +
        ylim(0, 6) +
        scale_fill_manual(values=c("#00000080","#80000080","#00008080","#80808080")) +
        geom_jitter(data = dataset, cex=1, position=position_dodge(0.9),  aes(colour=genotype)) +
        scale_color_manual(values=c("#000000","#800000","#000080","#808080")) +
        geom_errorbar(aes(ymin=NormOutcome2-se, ymax=NormOutcome2+se),width=.2, position=position_dodge(.9)) 

dev.off()

## quartz_off_screen 
##                 2

cairo_pdf("Figure2C_v3.pdf", width = 8, height = 4, family = "Arial")

ggplot(dataset, aes(x=treatment, y=NormOutcome2, color=genotype)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        #geom_bar(stat="identity", position=position_dodge()) +
        geom_jitter(cex=1, position=position_dodge(1)) + 
        stat_summary(fun.data=mean_se, fun.args = list(mult=1), geom="errorbar", width=0.25, aes(colour=genotype), position=position_dodge(1)) +
        stat_summary(fun.y=mean, geom="crossbar", width=0.5, aes(colour=genotype), position=position_dodge(1)) +
        ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
        xlab(label = "Olaparib (nM)") +
        ylim(0, 6) +
        scale_fill_manual(values=c("#000000","#800000","#000080","#808080")) +
        scale_color_manual(values=c("#000000","#800000","#000080","#808080"))

dev.off()

## quartz_off_screen 
##                 2

Linear Model

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

fit1 <- lm(NormOutcome2 ~ olaparib*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = NormOutcome2 ~ olaparib * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.24699 -0.24173  0.03446  0.25248  1.07686 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      0.8226     0.2236   3.679 0.000556 ***
## olaparib                         0.1808     0.0786   2.301 0.025445 *  
## genotypePARP1KO                  0.3265     0.3162   1.033 0.306487    
## genotypeALC1KO                   0.8901     0.3162   2.815 0.006873 ** 
## genotypeALC1KOPARP1KO            0.2228     0.3162   0.705 0.484186    
## olaparib:genotypePARP1KO        -0.2016     0.1112  -1.814 0.075516 .  
## olaparib:genotypeALC1KO          0.4993     0.1112   4.491 3.97e-05 ***
## olaparib:genotypeALC1KOPARP1KO  -0.2213     0.1112  -1.991 0.051766 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4734 on 52 degrees of freedom
## Multiple R-squared:  0.8652, Adjusted R-squared:  0.847 
## F-statistic: 47.66 on 7 and 52 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit1))

## AIC:  89.94364

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormOutcome2 ~ poly(olaparib,2)*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormOutcome2 ~ poly(olaparib, 2) * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67569 -0.16602 -0.01959  0.13902  0.81110 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               1.25329    0.08562  14.638  < 2e-16
## poly(olaparib, 2)1                        2.17830    0.66321   3.284  0.00191
## poly(olaparib, 2)2                        1.23615    0.66321   1.864  0.06846
## genotypePARP1KO                          -0.15359    0.12109  -1.268  0.21075
## genotypeALC1KO                            2.07911    0.12109  17.171  < 2e-16
## genotypeALC1KOPARP1KO                    -0.30427    0.12109  -2.513  0.01539
## poly(olaparib, 2)1:genotypePARP1KO       -2.42823    0.93792  -2.589  0.01270
## poly(olaparib, 2)2:genotypePARP1KO       -1.60138    0.93792  -1.707  0.09422
## poly(olaparib, 2)1:genotypeALC1KO         6.01356    0.93792   6.412 5.88e-08
## poly(olaparib, 2)2:genotypeALC1KO        -6.09689    0.93792  -6.500 4.29e-08
## poly(olaparib, 2)1:genotypeALC1KOPARP1KO -2.66556    0.93792  -2.842  0.00656
## poly(olaparib, 2)2:genotypeALC1KOPARP1KO -1.69364    0.93792  -1.806  0.07723
##                                             
## (Intercept)                              ***
## poly(olaparib, 2)1                       ** 
## poly(olaparib, 2)2                       .  
## genotypePARP1KO                             
## genotypeALC1KO                           ***
## genotypeALC1KOPARP1KO                    *  
## poly(olaparib, 2)1:genotypePARP1KO       *  
## poly(olaparib, 2)2:genotypePARP1KO       .  
## poly(olaparib, 2)1:genotypeALC1KO        ***
## poly(olaparib, 2)2:genotypeALC1KO        ***
## poly(olaparib, 2)1:genotypeALC1KOPARP1KO ** 
## poly(olaparib, 2)2:genotypeALC1KOPARP1KO .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3316 on 48 degrees of freedom
## Multiple R-squared:  0.9389, Adjusted R-squared:  0.9249 
## F-statistic: 67.08 on 11 and 48 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit2))

## AIC:  50.42708

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormOutcome2 ~ poly(olaparib,3)*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormOutcome2 ~ poly(olaparib, 3) * genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.68893 -0.17760 -0.01805  0.16931  0.84585 
## 
## Coefficients:
##                                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                               1.25329    0.08458  14.818  < 2e-16
## poly(olaparib, 3)1                        2.17830    0.65516   3.325  0.00179
## poly(olaparib, 3)2                        1.23615    0.65516   1.887  0.06580
## poly(olaparib, 3)3                        0.14096    0.65516   0.215  0.83065
## genotypePARP1KO                          -0.15359    0.11962  -1.284  0.20585
## genotypeALC1KO                            2.07911    0.11962  17.382  < 2e-16
## genotypeALC1KOPARP1KO                    -0.30427    0.11962  -2.544  0.01456
## poly(olaparib, 3)1:genotypePARP1KO       -2.42823    0.92654  -2.621  0.01200
## poly(olaparib, 3)2:genotypePARP1KO       -1.60138    0.92654  -1.728  0.09094
## poly(olaparib, 3)3:genotypePARP1KO        1.23345    0.92654   1.331  0.18996
## poly(olaparib, 3)1:genotypeALC1KO         6.01356    0.92654   6.490 6.41e-08
## poly(olaparib, 3)2:genotypeALC1KO        -6.09689    0.92654  -6.580 4.73e-08
## poly(olaparib, 3)3:genotypeALC1KO        -0.48503    0.92654  -0.523  0.60326
## poly(olaparib, 3)1:genotypeALC1KOPARP1KO -2.66556    0.92654  -2.877  0.00617
## poly(olaparib, 3)2:genotypeALC1KOPARP1KO -1.69364    0.92654  -1.828  0.07435
## poly(olaparib, 3)3:genotypeALC1KOPARP1KO -0.58714    0.92654  -0.634  0.52957
##                                             
## (Intercept)                              ***
## poly(olaparib, 3)1                       ** 
## poly(olaparib, 3)2                       .  
## poly(olaparib, 3)3                          
## genotypePARP1KO                             
## genotypeALC1KO                           ***
## genotypeALC1KOPARP1KO                    *  
## poly(olaparib, 3)1:genotypePARP1KO       *  
## poly(olaparib, 3)2:genotypePARP1KO       .  
## poly(olaparib, 3)3:genotypePARP1KO          
## poly(olaparib, 3)1:genotypeALC1KO        ***
## poly(olaparib, 3)2:genotypeALC1KO        ***
## poly(olaparib, 3)3:genotypeALC1KO           
## poly(olaparib, 3)1:genotypeALC1KOPARP1KO ** 
## poly(olaparib, 3)2:genotypeALC1KOPARP1KO .  
## poly(olaparib, 3)3:genotypeALC1KOPARP1KO    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3276 on 44 degrees of freedom
## Multiple R-squared:  0.9454, Adjusted R-squared:  0.9267 
## F-statistic: 50.75 on 15 and 44 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit3))

## AIC:  51.74104

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

Compare Results

ICtab(fit1,fit2,
      fit3,
      base=T)

##      AIC  dAIC df
## fit2 50.4  0.0 13
## fit3 51.7  1.3 17
## fit1 89.9 39.5 9

Final Result

fit <- fit2

output <- coef(summary(fit))
output <- output[grep("olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
olaparib1 in WT	2.178301	0.6632120	3.284471	0.0019121
olaparib2 in WT	1.236153	0.6632120	1.863888	0.0684602
olaparib1: WT vs. PARP1KO	-2.428227	0.9379234	-2.588939	0.0127045
olaparib2: WT vs. PARP1KO	-1.601379	0.9379234	-1.707367	0.0942155
olaparib1: WT vs. ALC1KO	6.013559	0.9379234	6.411567	0.0000001
olaparib2: WT vs. ALC1KO	-6.096891	0.9379234	-6.500414	0.0000000
olaparib1: WT vs. ALC1KOPARP1KO	-2.665563	0.9379234	-2.841984	0.0065634
olaparib2: WT vs. ALC1KOPARP1KO	-1.693642	0.9379234	-1.805735	0.0772327

write.table(output, file = "Figure2C_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1KO")

fit <- lm(NormOutcome2 ~ poly(olaparib,2)*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("olaparib", rownames(output)),]

rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
olaparib1 in ALC1KO	8.191859	0.6632120	12.351796	0.00e+00
olaparib2 in ALC1KO	-4.860738	0.6632120	-7.329087	0.00e+00
olaparib1: ALC1KO vs. WT	-6.013559	0.9379234	-6.411567	1.00e-07
olaparib2: ALC1KO vs. WT	6.096891	0.9379234	6.500414	0.00e+00
olaparib1: ALC1KO vs. PARP1KO	-8.441785	0.9379234	-9.000506	0.00e+00
olaparib2: ALC1KO vs. PARP1KO	4.495512	0.9379234	4.793048	1.63e-05
olaparib1: ALC1KO vs. ALC1KOPARP1KO	-8.679122	0.9379234	-9.253551	0.00e+00
olaparib2: ALC1KO vs. ALC1KOPARP1KO	4.403249	0.9379234	4.694679	2.26e-05

write.table(output, file = "Figure2C_Stats_Ref_ALC1KO.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

ANOVA

fit2a <- lm(NormOutcome2 ~ poly(olaparib,2)*genotype, data = dataset)
fit2b <- lm(NormOutcome2 ~ poly(olaparib,2)+genotype, data = dataset)

anova(fit2a, fit2b)

## Analysis of Variance Table
## 
## Model 1: NormOutcome2 ~ poly(olaparib, 2) * genotype
## Model 2: NormOutcome2 ~ poly(olaparib, 2) + genotype
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     48  5.2782                                  
## 2     54 22.6547 -6   -17.377 26.337 1.282e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

New analysis

• fit model for each conc. independently

dataset$genotype <- relevel(dataset$genotype, ref = "WT")


for(i in 2:length(levels(dataset$treatment))){
        
        dataset_sub <- dataset[dataset$treatment == levels(dataset$treatment)[i],]
        
        for(g in seq_along(levels(dataset_sub$genotype))){
                
                
                cat("Olaparib: ", levels(dataset$treatment)[i], "nM", "\n",
                    "Genotype: ", levels(dataset$genotype)[g], "\n")
                
                
                dataset_sub$genotype <- relevel(dataset_sub$genotype, ref = levels(dataset_sub$genotype)[g])

                fit_sub <- lm(NormOutcome2 ~ genotype, data = dataset_sub)
                
                print(summary(fit_sub))
                cat("AIC: ", AIC(fit_sub))
                
                
                simres <- simulateResiduals(fittedModel = fit_sub)
                plot(simres)
                
                if(i == 2 & g == 1){
                        output <- coef(summary(fit_sub))
                        output <- output[grep("genotype", rownames(output)),]
                        rownames(output) <- paste0(gsub("genotype",  paste0(" ",levels(dataset$genotype)[g], " vs. "), rownames(output)),
                                                   " in Olaparib ", levels(dataset$treatment)[i], "nM")
                } else {
                        outtmp <- coef(summary(fit_sub))
                        outtmp <- outtmp[grep("genotype", rownames(outtmp)),]
                        rownames(outtmp) <- paste0(gsub("genotype",  paste0(" ",levels(dataset$genotype)[g], " vs. "), rownames(outtmp)),
                                                   " in Olaparib ", levels(dataset$treatment)[i], "nM")
                        
                        output <- rbind(output,outtmp)
                        
                        if(g == length(levels(dataset_sub$genotype)) & i < length(levels(dataset$treatment))){
                                
                                output <- rbind(output, " ", colnames(output))
                        }
                }
                
        }
        
        
}

## Olaparib:  30 nM 
##  Genotype:  WT 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33591 -0.24586  0.02992  0.24330  0.39102 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            0.97592    0.17258   5.655 0.000479 ***
## genotypeALC1KO         2.23401    0.24407   9.153 1.64e-05 ***
## genotypePARP1KO        0.45500    0.24407   1.864 0.099284 .  
## genotypeALC1KOPARP1KO  0.01667    0.24407   0.068 0.947237    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared:  0.9339, Adjusted R-squared:  0.9091 
## F-statistic: 37.69 on 3 and 8 DF,  p-value: 4.565e-05
## 
## AIC:  10.20702

## Olaparib:  30 nM 
##  Genotype:  ALC1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33591 -0.24586  0.02992  0.24330  0.39102 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             3.2099     0.1726  18.600 7.20e-08 ***
## genotypeWT             -2.2340     0.2441  -9.153 1.64e-05 ***
## genotypePARP1KO        -1.7790     0.2441  -7.289 8.48e-05 ***
## genotypeALC1KOPARP1KO  -2.2173     0.2441  -9.085 1.73e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared:  0.9339, Adjusted R-squared:  0.9091 
## F-statistic: 37.69 on 3 and 8 DF,  p-value: 4.565e-05
## 
## AIC:  10.20702

## Olaparib:  30 nM 
##  Genotype:  PARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33591 -0.24586  0.02992  0.24330  0.39102 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.4309     0.1726   8.291 3.37e-05 ***
## genotypeALC1KO          1.7790     0.2441   7.289 8.48e-05 ***
## genotypeWT             -0.4550     0.2441  -1.864   0.0993 .  
## genotypeALC1KOPARP1KO  -0.4383     0.2441  -1.796   0.1102    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared:  0.9339, Adjusted R-squared:  0.9091 
## F-statistic: 37.69 on 3 and 8 DF,  p-value: 4.565e-05
## 
## AIC:  10.20702

## Olaparib:  30 nM 
##  Genotype:  ALC1KOPARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.33591 -0.24586  0.02992  0.24330  0.39102 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.99259    0.17258   5.751 0.000428 ***
## genotypePARP1KO  0.43833    0.24407   1.796 0.110234    
## genotypeALC1KO   2.21735    0.24407   9.085 1.73e-05 ***
## genotypeWT      -0.01667    0.24407  -0.068 0.947237    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared:  0.9339, Adjusted R-squared:  0.9091 
## F-statistic: 37.69 on 3 and 8 DF,  p-value: 4.565e-05
## 
## AIC:  10.20702

## Olaparib:  300 nM 
##  Genotype:  WT 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67328 -0.07370  0.03119  0.19254  0.38571 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.08982    0.19754   5.517 0.000562 ***
## genotypeALC1KO         2.99862    0.27936  10.734 4.99e-06 ***
## genotypePARP1KO       -0.02255    0.27936  -0.081 0.937650    
## genotypeALC1KOPARP1KO -0.13040    0.27936  -0.467 0.653115    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9412 
## F-statistic: 59.67 on 3 and 8 DF,  p-value: 8.101e-06
## 
## AIC:  13.44839

## Olaparib:  300 nM 
##  Genotype:  ALC1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67328 -0.07370  0.03119  0.19254  0.38571 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             4.0884     0.1975   20.70 3.11e-08 ***
## genotypeWT             -2.9986     0.2794  -10.73 4.99e-06 ***
## genotypePARP1KO        -3.0212     0.2794  -10.81 4.72e-06 ***
## genotypeALC1KOPARP1KO  -3.1290     0.2794  -11.20 3.62e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9412 
## F-statistic: 59.67 on 3 and 8 DF,  p-value: 8.101e-06
## 
## AIC:  13.44839

## Olaparib:  300 nM 
##  Genotype:  PARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67328 -0.07370  0.03119  0.19254  0.38571 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            1.06727    0.19754   5.403 0.000644 ***
## genotypeALC1KO         3.02117    0.27936  10.815 4.72e-06 ***
## genotypeWT             0.02255    0.27936   0.081 0.937650    
## genotypeALC1KOPARP1KO -0.10785    0.27936  -0.386 0.709523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9412 
## F-statistic: 59.67 on 3 and 8 DF,  p-value: 8.101e-06
## 
## AIC:  13.44839

## Olaparib:  300 nM 
##  Genotype:  ALC1KOPARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.67328 -0.07370  0.03119  0.19254  0.38571 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.9594     0.1975   4.857  0.00126 ** 
## genotypePARP1KO   0.1078     0.2794   0.386  0.70952    
## genotypeALC1KO    3.1290     0.2794  11.201 3.62e-06 ***
## genotypeWT        0.1304     0.2794   0.467  0.65311    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared:  0.9572, Adjusted R-squared:  0.9412 
## F-statistic: 59.67 on 3 and 8 DF,  p-value: 8.101e-06
## 
## AIC:  13.44839

## Olaparib:  3000 nM 
##  Genotype:  WT 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.29187 -0.18368 -0.03196  0.17976  0.41146 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.3598     0.1578   8.615 2.55e-05 ***
## genotypeALC1KO          3.0475     0.2232  13.652 7.98e-07 ***
## genotypePARP1KO        -0.4917     0.2232  -2.203   0.0587 .  
## genotypeALC1KOPARP1KO  -0.3015     0.2232  -1.351   0.2137    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared:  0.9767, Adjusted R-squared:  0.9679 
## F-statistic: 111.7 on 3 and 8 DF,  p-value: 7.203e-07
## 
## AIC:  8.064594

## Olaparib:  3000 nM 
##  Genotype:  ALC1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.29187 -0.18368 -0.03196  0.17976  0.41146 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             4.4074     0.1578   27.92 2.92e-09 ***
## genotypeWT             -3.0475     0.2232  -13.65 7.98e-07 ***
## genotypePARP1KO        -3.5393     0.2232  -15.86 2.51e-07 ***
## genotypeALC1KOPARP1KO  -3.3491     0.2232  -15.00 3.85e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared:  0.9767, Adjusted R-squared:  0.9679 
## F-statistic: 111.7 on 3 and 8 DF,  p-value: 7.203e-07
## 
## AIC:  8.064594

## Olaparib:  3000 nM 
##  Genotype:  PARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.29187 -0.18368 -0.03196  0.17976  0.41146 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.8681     0.1578   5.500 0.000574 ***
## genotypeALC1KO          3.5393     0.2232  15.855 2.51e-07 ***
## genotypeWT              0.4917     0.2232   2.203 0.058737 .  
## genotypeALC1KOPARP1KO   0.1902     0.2232   0.852 0.418951    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared:  0.9767, Adjusted R-squared:  0.9679 
## F-statistic: 111.7 on 3 and 8 DF,  p-value: 7.203e-07
## 
## AIC:  8.064594

## Olaparib:  3000 nM 
##  Genotype:  ALC1KOPARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.29187 -0.18368 -0.03196  0.17976  0.41146 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.0583     0.1578   6.705 0.000152 ***
## genotypePARP1KO  -0.1902     0.2232  -0.852 0.418951    
## genotypeALC1KO    3.3491     0.2232  15.003 3.85e-07 ***
## genotypeWT        0.3015     0.2232   1.351 0.213743    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared:  0.9767, Adjusted R-squared:  0.9679 
## F-statistic: 111.7 on 3 and 8 DF,  p-value: 7.203e-07
## 
## AIC:  8.064594

## Olaparib:  30000 nM 
##  Genotype:  WT 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45781 -0.39803  0.01569  0.20417  0.84873 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.8409     0.2732   6.739 0.000147 ***
## genotypeALC1KO          2.1154     0.3863   5.476 0.000590 ***
## genotypePARP1KO        -0.7087     0.3863  -1.835 0.103906    
## genotypeALC1KOPARP1KO  -1.1061     0.3863  -2.863 0.021041 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared:  0.9119, Adjusted R-squared:  0.8789 
## F-statistic:  27.6 on 3 and 8 DF,  p-value: 0.000143
## 
## AIC:  21.22689

## Olaparib:  30000 nM 
##  Genotype:  ALC1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45781 -0.39803  0.01569  0.20417  0.84873 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             3.9562     0.2732  14.484 5.05e-07 ***
## genotypeWT             -2.1154     0.3863  -5.476  0.00059 ***
## genotypePARP1KO        -2.8240     0.3863  -7.311 8.30e-05 ***
## genotypeALC1KOPARP1KO  -3.2215     0.3863  -8.339 3.23e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared:  0.9119, Adjusted R-squared:  0.8789 
## F-statistic:  27.6 on 3 and 8 DF,  p-value: 0.000143
## 
## AIC:  21.22689

## Olaparib:  30000 nM 
##  Genotype:  PARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45781 -0.39803  0.01569  0.20417  0.84873 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             1.1322     0.2732   4.145  0.00323 ** 
## genotypeALC1KO          2.8240     0.3863   7.311  8.3e-05 ***
## genotypeWT              0.7087     0.3863   1.835  0.10391    
## genotypeALC1KOPARP1KO  -0.3974     0.3863  -1.029  0.33366    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared:  0.9119, Adjusted R-squared:  0.8789 
## F-statistic:  27.6 on 3 and 8 DF,  p-value: 0.000143
## 
## AIC:  21.22689

## Olaparib:  30000 nM 
##  Genotype:  ALC1KOPARP1KO 
## 
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45781 -0.39803  0.01569  0.20417  0.84873 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       0.7347     0.2732   2.690   0.0275 *  
## genotypePARP1KO   0.3974     0.3863   1.029   0.3337    
## genotypeALC1KO    3.2215     0.3863   8.339 3.23e-05 ***
## genotypeWT        1.1061     0.3863   2.863   0.0210 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared:  0.9119, Adjusted R-squared:  0.8789 
## F-statistic:  27.6 on 3 and 8 DF,  p-value: 0.000143
## 
## AIC:  21.22689

# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
WT vs. ALC1KO in Olaparib 30nM	2.23401467145659	0.244066996853167	9.153284549982	1.63647502322034e-05
WT vs. PARP1KO in Olaparib 30nM	0.454996688324251	0.244066996853167	1.86422865111083	0.0992836079937305
WT vs. ALC1KOPARP1KO in Olaparib 30nM	0.0166654665999983	0.244066996853167	0.0682823438435818	0.947236691399539
ALC1KO vs. WT in Olaparib 30nM	-2.23401467145659	0.244066996853167	-9.153284549982	1.63647502322033e-05
ALC1KO vs. PARP1KO in Olaparib 30nM	-1.77901798313234	0.244066996853167	-7.28905589887117	8.47805458400702e-05
ALC1KO vs. ALC1KOPARP1KO in Olaparib 30nM	-2.2173492048566	0.244066996853167	-9.08500220613842	1.72928582043792e-05
PARP1KO vs. ALC1KO in Olaparib 30nM	1.77901798313234	0.244066996853167	7.28905589887116	8.47805458400707e-05
PARP1KO vs. WT in Olaparib 30nM	-0.454996688324251	0.244066996853167	-1.86422865111083	0.0992836079937302
PARP1KO vs. ALC1KOPARP1KO in Olaparib 30nM	-0.438331221724253	0.244066996853167	-1.79594630726725	0.110233745608729
ALC1KOPARP1KO vs. PARP1KO in Olaparib 30nM	0.438331221724252	0.244066996853167	1.79594630726725	0.110233745608729
ALC1KOPARP1KO vs. ALC1KO in Olaparib 30nM	2.2173492048566	0.244066996853167	9.08500220613841	1.72928582043794e-05
ALC1KOPARP1KO vs. WT in Olaparib 30nM	-0.0166654665999985	0.244066996853167	-0.0682823438435824	0.947236691399538
	Estimate	Std. Error	t value	Pr(>|t|)
WT vs. ALC1KO in Olaparib 300nM	2.99862474465302	0.279359527256207	10.7339269009534	4.99039951809161e-06
WT vs. PARP1KO in Olaparib 300nM	-0.0225488104208594	0.279359527256207	-0.080716096001191	0.937650484949544
WT vs. ALC1KOPARP1KO in Olaparib 300nM	-0.130395816857611	0.279359527256207	-0.466767030064529	0.653114521477612
ALC1KO vs. WT in Olaparib 300nM	-2.99862474465302	0.279359527256207	-10.7339269009534	4.99039951809162e-06
ALC1KO vs. PARP1KO in Olaparib 300nM	-3.02117355507389	0.279359527256207	-10.8146429969546	4.71648981626386e-06
ALC1KO vs. ALC1KOPARP1KO in Olaparib 300nM	-3.12902056151064	0.279359527256207	-11.2006939310179	3.61876268369268e-06
PARP1KO vs. ALC1KO in Olaparib 300nM	3.02117355507388	0.279359527256207	10.8146429969546	4.71648981626387e-06
PARP1KO vs. WT in Olaparib 300nM	0.0225488104208593	0.279359527256207	0.0807160960011909	0.937650484949544
PARP1KO vs. ALC1KOPARP1KO in Olaparib 300nM	-0.107847006436751	0.279359527256207	-0.386050934063337	0.709523006620542
ALC1KOPARP1KO vs. PARP1KO in Olaparib 300nM	0.107847006436751	0.279359527256207	0.386050934063337	0.709523006620542
ALC1KOPARP1KO vs. ALC1KO in Olaparib 300nM	3.12902056151064	0.279359527256207	11.2006939310179	3.61876268369267e-06
ALC1KOPARP1KO vs. WT in Olaparib 300nM	0.130395816857611	0.279359527256207	0.466767030064529	0.653114521477611
	Estimate	Std. Error	t value	Pr(>|t|)
WT vs. ALC1KO in Olaparib 3000nM	3.04754058178664	0.223223796352674	13.6524001095823	7.97659310937146e-07
WT vs. PARP1KO in Olaparib 3000nM	-0.491717229051973	0.223223796352674	-2.20279933002798	0.0587369379418308
WT vs. ALC1KOPARP1KO in Olaparib 3000nM	-0.301514965032195	0.223223796352674	-1.35072949192132	0.213742755890244
ALC1KO vs. WT in Olaparib 3000nM	-3.04754058178663	0.223223796352675	-13.6524001095823	7.9765931093716e-07
ALC1KO vs. PARP1KO in Olaparib 3000nM	-3.53925781083861	0.223223796352675	-15.8551994396103	2.50525637047875e-07
ALC1KO vs. ALC1KOPARP1KO in Olaparib 3000nM	-3.34905554681883	0.223223796352675	-15.0031296015036	3.84720496457958e-07
PARP1KO vs. ALC1KO in Olaparib 3000nM	3.53925781083861	0.223223796352674	15.8551994396103	2.50525637047871e-07
PARP1KO vs. WT in Olaparib 3000nM	0.491717229051973	0.223223796352674	2.20279933002798	0.0587369379418306
PARP1KO vs. ALC1KOPARP1KO in Olaparib 3000nM	0.190202264019778	0.223223796352674	0.852069838106663	0.418950633286543
ALC1KOPARP1KO vs. PARP1KO in Olaparib 3000nM	-0.190202264019778	0.223223796352674	-0.852069838106662	0.418950633286544
ALC1KOPARP1KO vs. ALC1KO in Olaparib 3000nM	3.34905554681883	0.223223796352674	15.0031296015037	3.84720496457951e-07
ALC1KOPARP1KO vs. WT in Olaparib 3000nM	0.301514965032195	0.223223796352674	1.35072949192132	0.213742755890244
	Estimate	Std. Error	t value	Pr(>|t|)
WT vs. ALC1KO in Olaparib 30000nM	2.11535269897002	0.386296014823517	5.47598892506422	0.000590250711051459
WT vs. PARP1KO in Olaparib 30000nM	-0.708690088633984	0.386296014823517	-1.8345777886364	0.10390557266586
WT vs. ALC1KOPARP1KO in Olaparib 30000nM	-1.10612544718416	0.386296014823517	-2.86341407816363	0.0210407342023467
ALC1KO vs. WT in Olaparib 30000nM	-2.11535269897002	0.386296014823516	-5.47598892506421	0.000590250711051459
ALC1KO vs. PARP1KO in Olaparib 30000nM	-2.824042787604	0.386296014823516	-7.31056671370062	8.30354874963266e-05
ALC1KO vs. ALC1KOPARP1KO in Olaparib 30000nM	-3.22147814615418	0.386296014823517	-8.33940300322785	3.23405238481284e-05
PARP1KO vs. ALC1KO in Olaparib 30000nM	2.824042787604	0.386296014823517	7.31056671370062	8.30354874963269e-05
PARP1KO vs. WT in Olaparib 30000nM	0.708690088633983	0.386296014823517	1.8345777886364	0.10390557266586
PARP1KO vs. ALC1KOPARP1KO in Olaparib 30000nM	-0.397435358550181	0.386296014823517	-1.02883628952722	0.333655624920083
ALC1KOPARP1KO vs. PARP1KO in Olaparib 30000nM	0.39743535855018	0.386296014823517	1.02883628952722	0.333655624920084
ALC1KOPARP1KO vs. ALC1KO in Olaparib 30000nM	3.22147814615418	0.386296014823517	8.33940300322784	3.23405238481286e-05
ALC1KOPARP1KO vs. WT in Olaparib 30000nM	1.10612544718416	0.386296014823517	2.86341407816363	0.0210407342023467

write.table(output, file = "Figure2C_Stats_New_All.txt", quote = F, sep = "\t", row.names = T, col.names = NA)